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- W2945492523 abstract "© Civil-Comp Press, 2015. Condition assessment of roadway transport networks evaluates the vulnerability of road segments and by extension the future reliability of such networks. Traditional road pavement condition assessment involves manual effort, which requires training, is costly and time-consuming given the quantity of road needing assessment, and poses a risk to the safety of inspectors involved. Automated roadway assessment methods using computer vision can overcome these limitations. In this paper, the detection of type distresses in images, taken from video cameras positioned on vehicles is presented. This method is based on support vector machine (SVM) classification applied to the image data. The process first divides a test image into square blocks, then generates a feature vector from the block to be used in the SVM. Two feature vector types are tested: histogram and texture using the discrete cosine transform. The result is an output with each image block being classified as patch or no-patch. The recognition results of the case study from a real-life urban network are promising, showing a detection accuracy of about 81%, based on a MatlabTM implementation." @default.
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- W2945492523 date "2015-08-06" @default.
- W2945492523 modified "2023-09-26" @default.
- W2945492523 title "Patch Defects in Images using Support Vector Machines" @default.
- W2945492523 doi "https://doi.org/10.4203/ccp.108.283" @default.
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